What is variational inference and how does it define cost functions?

Variational inference approximates complex distributions by minimizing divergence-based cost functions.

Variational inference transforms probabilistic inference into optimization by defining a cost function based on KL divergence between approximate and true posterior distributions. The goal is to minimize evidence lower bound (ELBO), balancing reconstruction accuracy and regularization.